Static and dynamic features for writer identification based on multi-fractals

Writer identification still remains as a challenge area in the field of off-line handwriting recognition because only an image of the handwriting is available. Consequently, some information on the dynamic of writing, which is valuable for identification of writer, is unavailable in the off-line approaches, contrary to the on-line ones where temporal and spatial information about the writing are available. In this paper, we present a new method for writer identification based on multifractal features for both types of presented approaches. This method consists to extract the multi-fractal dimensions from the images of words and their on-line signals. In order to enhance the performance of our writer identification system, we have combined both on-line and off-line approaches. In this way, our work consists to take advantage of static and dynamic representations of handwriting, in order to identify the writer in realistic conditions. The tests are performed on the writing of 110 writers from the ADAB database. The obtained results show the effectiveness of the proposed writer identification method.